Spss statistical analysis software package

To determine whether there was a correlation between categorical factors and the occurrence of Salmonella, AMR, and MDR, a generalized linear model was applied to the data (reptile species, habitat, sample type, diet, and number of reptiles cohabiting the same terrarium). If Salmonella was detected in the oral cavity, skin, or cloaca of a reptile, it was considered positive. For a statistically significant difference, the threshold was p ≤ 0.05. The results were analyzed using basic descriptive statistics. The 95 per cent confidence intervals (95% CI) for the Salmonella enterica strains isolated from reptiles were calculated and estimated to compare different factors. The obtained results were statistically interpreted using the SPSS statistical analysis software package, version 28.0.1.1, Chicago, USA. A nonparametric Pearson’s chi-squared (χ²) test was used to find any possible associations between the Salmonella infection status and the recorded epidemiological data. Differences were established as statistically significant when the p-value  ≤ 0.05.

Statistical analysis was performed with the SPSS statistical analysis software package. Based on the normality of the distributions, the Mann–Whitney U test was used to assess the statistical significance of the differences between the covariates. The Spearman rank correlation test was used to assess the relationship between IMNI dose differences and the covariates. All tests were two-sided. The results were regarded as statistically significant when P was <0.05.

Continuous variables were summarized as the standard error of the mean (SEM). For continuous variables, statistical comparisons between control and treated groups were established by carrying out one-way ANOVA tests or the Student's t-test (in cases where there were two independent groups). Dichotomous variables were summarized by absolute and/or relative frequencies. For dichotomous variables, statistical comparisons between control and treated groups were established by carrying out Fisher's exact test. For multiple comparisons, the level of significance was corrected by multiplying the P value by the number of comparisons performed (n) according to the Bonferroni correction. All tests were two-sided and were determined by Monte Carlo significance. P values less than 0.05 were considered statistically significant. All statistical analyses were performed using the SPSS^® statistical analysis software package, version 10.0 [21 (link)].

The obtained results were statistically interpreted using the SPSS statistical analysis software package, version 21.0. A nonparametric Pearson’s chi-squared (χ²) test was used in order to find any possible associations between the Salmonella infection status and the recorded epidemiological data. Differences were established as statistically significant when p-value  ≤  0.05.

Statistical analysis was performed with the SPSS statistical analysis software package. Based on the normality of the distributions, t-tests or one-way analysis of variance (ANOVA) was used to assess the statistical significance of the differences between the covariates. Univariate regression analysis and multiple regression analysis were used to assess the relationship between IIMNI dose differences and a set of covariates, such as body weight, BMI, BSA, and radiotherapy technique. All tests were two-sided. Results were regarded as statistically significant when p < 0.05.